Pakistan PK: Current Health Expenditure: % of GDP data was reported at 2.689 % in 2015. This records a decrease from the previous number of 2.722 % for 2014. Pakistan PK: Current Health Expenditure: % of GDP data is updated yearly, averaging 2.706 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 3.343 % in 2007 and a record low of 2.357 % in 2011. Pakistan PK: Current Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Health Statistics. Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Historical dataset showing Pakistan healthcare spending per capita by year from 2000 to 2022.

Pakistan PK: Domestic Private Health Expenditure: % of Current Health Expenditure data was reported at 68.794 % in 2015. This records a decrease from the previous number of 68.806 % for 2014. Pakistan PK: Domestic Private Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 68.979 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 80.476 % in 2006 and a record low of 60.271 % in 2002. Pakistan PK: Domestic Private Health Expenditure: % of Current Health Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Health Statistics. Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Dataset Description: Pakistan Hospitals Dataset

This comprehensive dataset provides a detailed record of 8,650 hospitals located in various cities and areas across Pakistan. It encompasses essential information about each hospital, facilitating insights into the country's healthcare infrastructure. The dataset covers a wide range of hospitals, offering a valuable resource for healthcare analysts, researchers, and policymakers.

Features

Hospital Name: The name of the hospital. City: The city where the hospital is located. Area: The specific area within the city where the hospital is situated. Available Doctors: The count of doctors available at the hospital. Address: The physical address of the hospital's location. Contact: Contact information for the hospital, such as phone numbers or email addresses. The dataset aims to empower data-driven decisions and in-depth analyses related to healthcare accessibility, distribution of medical facilities, and trends in the healthcare landscape of Pakistan. By providing a comprehensive view of hospitals and their associated attributes, this dataset encourages exploration of healthcare patterns and their implications.

Potential Use Cases

Analyzing the distribution of hospitals and doctors across different cities and areas. Identifying areas with high or low doctor-to-patient ratios. Assessing the correlation between hospital size and the number of available doctors. Exploring trends in healthcare infrastructure growth over time. Supporting decision-making processes for healthcare investment and resource allocation.

Data Source

The dataset has been curated from reliable sources, ensuring accuracy and relevance. It offers a robust foundation for conducting meaningful analyses and contributing to the understanding of healthcare dynamics in Pakistan.

Pakistan PK: Domestic Private Health Expenditure Per Capita: Current Price data was reported at 0.000 USD mn in 2015. This records an increase from the previous number of 0.000 USD mn for 2014. Pakistan PK: Domestic Private Health Expenditure Per Capita: Current Price data is updated yearly, averaging 0.000 USD mn from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 0.000 USD mn in 2015 and a record low of 0.000 USD mn in 2001. Pakistan PK: Domestic Private Health Expenditure Per Capita: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Health Statistics. Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted average;

This dataset is about countries per year in Pakistan. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, vulnerable employment, and health expenditure.

Healthcare fraud is considered a challenge for many societies. Health care funding that could be spent on medicine, care for the elderly, or emergency room visits is instead lost to fraudulent activities by materialistic practitioners or patients. With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs.

Try out various unsupervised techniques to find the anomalies in the data.

Detailed Data File:

The following variables are included in the detailed Physician and Other Supplier data file (see Appendix A for a condensed version of variables included)).

npi – National Provider Identifier (NPI) for the performing provider on the claim. The provider NPI is the numeric identifier registered in NPPES.

nppes_provider_last_org_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s last name. When the provider is registered as an organization (entity type code = ‘O’), this is the organization's name.

nppes_provider_first_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s first name. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_mi – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s middle initial. When the provider is registered as an organization (entity type code= ‘O’), this will be blank.

nppes_credentials – When the provider is registered in NPPES as an individual (entity type code=’I’), these are the provider’s credentials. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_gender – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s gender. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_entity_code – Type of entity reported in NPPES. An entity code of ‘I’ identifies providers registered as individuals and an entity type code of ‘O’ identifies providers registered as organizations.

nppes_provider_street1 – The first line of the provider’s street address, as reported in NPPES.

nppes_provider_street – The second line of the provider’s street address, as reported in NPPES.

nppes_provider_city – The city where the provider is located, as reported in NPPES.

nppes_provider_zip – The provider’s zip code, as reported in NPPES.

nppes_provider_state – The state where the provider is located, as reported in NPPES. The fifty U.S. states and the District of Columbia are reported by the state postal abbreviation. The following values are used for all other areas:

'XX' = 'Unknown' 'AA' = 'Armed Forces Central/South America' 'AE' = 'Armed Forces Europe' 'AP' = 'Armed Forces Pacific' 'AS' = 'American Samoa' 'GU' = 'Guam' 'MP' = 'North Mariana Islands' 'PR' = 'Puerto Rico' 'VI' = 'Virgin Islands' 'ZZ' = 'Foreign Country'

nppes_provider_country – The country where the provider is located, as reported in NPPES. The country code will be ‘US’ for any state or U.S. possession. For foreign countries (i.e., state values of ‘ZZ’), the provider country values include the following: AE=United Arab Emirates IT=Italy AG=Antigua JO= Jordan AR=Argentina JP=Japan AU=Australia KR=Korea BO=Bolivia KW=Kuwait BR=Brazil KY=Cayman Islands CA=Canada LB=Lebanon CH=Switzerland MX=Mexico CN=China NL=Netherlands CO=Colombia NO=Norway DE= Germany NZ=New Zealand ES= Spain PA=Panama FR=France PK=Pakistan GB=Great Britain RW=Rwanda GR=Greece SA=Saudi Arabia HU= Hungary SY=Syria IL= Israel TH=Thailand IN=India TR=Turkey IS= Iceland VE=Venezuela

provider_type – Derived from the provider specialty code reported on the claim.

medicare_participation_indicator – Identifies whether the provider participates in Medicare and/or accepts the assigned assignment of Medicare allowed amounts.

place_of_service – Identifies whether the place of service submitted on the claims is a facility (value of ‘F’) or non-facility (value of ‘O’). Non-facility is generally an office setting; however other entities are included in non-facility.

hcpcs_code – HCPCS code used to identify the specific medical service furnished by the provider.

hcpcs_description – Description of the HCPCS code for the specific medical service furnished by the provider.

hcpcs_drug_indicator –Identifies whether the HCPCS code for the specific service furnished by the provider is an HCPCS listed on the Medicare Part B Drug Average Sales Price (ASP) File.

line_srvc_cnt – Number of services provided; note that the metrics used to count the number provided can vary from service to service.

bene_unique_cnt – Number of distinct Medicare beneficiaries rec...

ALHASAN SYSTEMS makes Pakistan Government Health Facilities data public through HDX under its Open Data/ Open Access [OD/OA] pioneering initiative. This data is used thoroughly in Pakistan by many stakeholders and researchers including UN and other donor agencies. For further details on the use of this data please download Alhasan Systems monthly Health Bulletins from [http://www.alhasan.com/bulletins/health].

Introduction

This Urdu Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Urdu speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Urdu speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Pakistan ranked 154th among 195 countries in terms of the Healthcare Access and Quality index, according to a Lancet study.

The Dataset provides comprehensive information about Pakistani doctors, encompassing various key aspects to aid in understanding and evaluating healthcare professionals. This dataset aims to provide valuable insights into the profiles of Pakistani doctors, facilitating informed decision-making for both healthcare professionals and patients.

Data Source: The data is collected using Web-Scraping and Crawling Techniques from a leading Pakistani healthcare consultation platform Marham.pk

Columns Description: The dataset includes the following essential columns:

Doctor Name: The full name of the healthcare professional.

City: The city in which the doctor's clinic is based, providing geographical context.

Specialization: The specific area of medical expertise or specialization of the doctor.

Doctor Qualification: The highest educational qualification attained by the doctor.

Experience(Years): The total number of years of professional experience the doctor has accumulated.

Total_Reviews: The cumulative number of reviews given by patients, reflecting the overall feedback received.

Patient Satisfaction Rate(%age): The percentage indicating the level of satisfaction reported by patients who have received medical care from the doctor.

Avg Time to Patients(mins): The average amount of time the doctor spends with each patient during consultations.

Wait Time(mins): The average waiting time experienced by patients at the clinic before their consultation.

Hospital Address: The name and location of the hospital or clinic where the doctor practices.

Doctors Link: An online access link or platform where patients can connect with the doctor, reflecting the growing trend of digital healthcare.

Doctor Fee(PKR): The fee charged by the doctor for their services, measured in Pakistani Rupees (PKR).

               **General Use Cases:**

Fee Prediction: Predicting the fees of doctors based on factors such as experience, qualification, and patient satisfaction rate. Wait Time Estimation: Forecasting the average wait time at a clinic, helping patients plan their appointments more efficiently. Patient Satisfaction Analysis: Understanding the factors influencing patient satisfaction and identifying areas for improvement.

              **Advanced Use Cases:**

Optimizing Appointment Scheduling: Developing algorithms to optimize the scheduling of appointments, considering factors like doctor availability and patient preferences. Healthcare Resource Allocation: Predicting future demand for specific specializations in different cities, assisting in the allocation of healthcare resources effectively. Personalized Patient Recommendations: Utilizing patient reviews and satisfaction rates to recommend doctors to new patients based on their preferences and requirements.

      **3. Where the Regression model can be used:**

Healthcare Platforms: Integrate the model into online healthcare platforms and directories to assist patients in finding suitable doctors. Hospital Management: Support hospital management in resource allocation, appointment scheduling, and fee structure decisions. Telemedicine Services: Enhance telemedicine services by providing personalized doctor recommendations and predicting patient needs. Health Insurance: Assist health insurance companies in assessing risk factors and setting appropriate premium rates based on the predicted healthcare demand in different regions. Government Health Planning: Aid government health agencies in strategic planning, ensuring optimal distribution of healthcare resources across regions.

Dataset Description: Lab Data Extracted from Marham.pk This dataset contains comprehensive details about various medical tests and laboratory services extracted from Marham.pk, a popular healthcare platform in Pakistan. The dataset provides structured information on different tests, their types, pricing, locations, and other relevant attributes.

Key Features of the Dataset The dataset includes the following important attributes:

Test Name – The name of the medical test (e.g., CBC, Lipid Profile, Blood Sugar Test). Test Type – The category or type of test (e.g., Blood Test, Radiology, Urine Test). Lab Name – The name of the laboratory offering the test. Lab Location – The city or specific area where the lab is located. Test Price – The cost of the test in Pakistani Rupees (PKR). Test Description – A brief overview of the purpose and details of the test. Availability – Indicates whether the test is available in a specific lab. Sample Requirements – Specifies whether fasting, urine, or blood samples are needed. Processing Time – The estimated time required to complete and deliver the test results. Discounts & Offers – Any special discounts provided by labs. Potential Use Cases This dataset can be utilized for various applications, including:

✅ Healthcare Analysis – Understanding pricing trends and availability of medical tests across different regions. ✅ Price Comparison – Comparing the cost of tests across multiple labs. ✅ Medical App Development – Integrating with health applications for users to find and book tests online. ✅ Data Visualization – Creating dashboards to display test availability and pricing insights. ✅ Predictive Analytics – Analyzing trends to predict future demands for medical tests.

Introduction

Introducing the Urdu Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of Urdu language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in Urdu, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

Using a sample of 1,211 households in Pakistan, we examine the effects of COVID-19 on three key domains: education, economic, and health-related. First, during school closures, 66 percent of surveyed households report not using technology for learning at all. Wealth disparities mar access to distance learning, and richer households are 39 percent more likely to use technology for learning compared to the poorest households. This has implications for learning remediation as children head back to school. Second, more than half of the respondents report a reduction in income and one-fifth report being food insecure during the lockdown in the first week of May, 2020. Only one-fifth of households reporting a reduction in income and one-fifth of respondents reporting a reduction in the number of meals consumed report being covered by the federal government’s cash transfer program. Third, while a majority of respondents (90 percent) report adopting precautionary measures such as face masks, a vast majority of respondents (78 percent) underestimate the risk of contracting a COVID-19 infection compared to tuberculosis. With schools reopening in a phased manner since mid-September, most respondents (68 percent) believe that school reopenings will further increase the risk of COVID-19 infections. (2020)

About the Dataset: Pharmaceutical Products Pricing and Availability Data in Pakistan

This dataset contains information about pharmaceutical product pricing and availability in Pakistan. The data was collected from various sources and compiled into a structured format for analysis. The dataset consists of 1630 entries with 7 columns, including:

Name: The name of the pharmaceutical product. Company: The company manufacturing or distributing the product. Price_before: The product's price before any discount is applied. Discount: The discount offered on the product, if applicable. Price_After: The price of the product after applying any discount. Pack_Size: The size or quantity of the product's packaging. Availability: The availability status of the product.

The dataset provides insights into the pricing trends and availability of pharmaceutical products in Pakistan, which can be valuable for various stakeholders including consumers, healthcare professionals, and policymakers. It can be used for analysis, research, and decision-making in the pharmaceutical industry.

Data Overview: Entries: 1630 Missing Values: Some columns have missing values, such as 'Name', 'Company', 'Price_before', 'Discount', 'Price_After', 'Pack_Size', and 'Availability'. Data Types: The dataset consists of object types for textual data and one float type for numerical data.

Potential Uses: This dataset can be used for a variety of purposes, including:

• Analyzing pricing trends and patterns of different drugs in Pakistan.
• Studying the affordability and accessibility of essential medicines in Pakistan.
• Tracking the performance of different pharmaceutical companies in the Pakistani market.
• Identifying potential areas for improving access to affordable medicines in Pakistan.

Limitations: It is important to note that this dataset only includes data on the maximum retail prices of pharmaceutical products. The actual price consumers pay may vary depending on the pharmacy and other factors. Additionally, the dataset does not include information on the quality of the pharmaceutical products.

I hope this description is helpful!

The Pakistan Demographic and Health Survey (PDHS) was fielded on a national basis between the months of December 1990 and May 1991. The survey was carried out by the National Institute of Population Studies with the objective of assisting the Ministry of Population Welfare to evaluate the Population Welfare Programme and maternal and child health services. The PDHS is the latest in a series of surveys, making it possible to evaluate changes in the demographic status of the population and in health conditions nationwide. Earlier surveys include the Pakistan Contraceptive Prevalence Survey of 1984-85 and the Pakistan Fertility Survey of 1975. The primary objective of the Pakistan Demographic and Health Survey (PDHS) was to provide national- and provincial-level data on population and health in Pakistan. The primary emphasis was on the following topics: fertility, nuptiality, family size preferences, knowledge and use of family planning, the potential demand for contraception, the level of unwanted fertility, infant and child mortality, breastfeeding and food supplementation practices, maternal care, child nutrition and health, immunisations and child morbidity. This information is intended to assist policy makers, administrators and researchers in assessing and evaluating population and health programmes and strategies. The PDHS is further intended to serve as a source of demographic data for comparison with earlier surveys, particularly the 1975 Pakistan Fertility Survey (PFS) and the 1984-85 Pakistan Contraceptive Prevalence Survey (PCPS). MAIN RESULTS Until recently, fertility rates had remained high with little evidence of any sustained fertility decline. In recent years, however, fertility has begun to decline due to a rapid increase in the age at marriage and to a modest rise in the prevalence of contraceptive use. The lotal fertility rate is estimated to have fallen from a level of approximately 6.4 children in the early 1980s to 6.0 children in the mid-1980s, to 5.4 children in the late 1980s. The exact magnitude of the change is in dispute and will be the subject of further research. Important differentials of fertility include the degree ofurbanisation and the level of women's education. The total fertility rate is estimated to be nearly one child lower in major cities (4.7) than in rural areas (5.6). Women with at least some secondary schooling have a rate of 3.6, compared to a rate of 5.7 children for women with no formal education. There is a wide disparity between women's knowledge and use of contraceptives in Pakistan. While 78 percent of currently married women report knowing at least one method of contraception, only 21 percent have ever used a method, and only 12 percent are currently doing so. Three-fourths of current users are using a modem method and one-fourth a traditional method. The two most commonly used methods are female sterilisation (4 percent) and the condom (3 percent). Despite the relatively low level of contraceptive use, the gain over time has been significant. Among married non-pregnant women, contraceptive use has almost tripled in 15 years, from 5 percent in 1975 to 14 percent in 1990-91. The contraceptive prevalence among women with secondary education is 38 percent, and among women with no schooling it is only 8 percent. Nearly one-third of women in major cities arc current users of contraception, but contraceptive use is still rare in rural areas (6 percent). The Government of Pakistan plays a major role in providing family planning services. Eighty-five percent of sterilised women and 81 percent of IUD users obtained services from the public sector. Condoms, however, were supplied primarily through the social marketing programme. The use of contraceptives depends on many factors, including the degree of acceptability of the concept of family planning. Among currently married women who know of a contraceptive method, 62 percent approve of family planning. There appears to be a considerable amount of consensus between husbands and wives about family planning use: one-third of female respondents reported that both they and their husbands approve of family planning, while slightly more than one-fifth said they both disapprove. The latter couples constitute a group for which family planning acceptance will require concerted motivational efforts. The educational levels attained by Pakistani women remain low: 79 percent of women have had no formal education, 14 percent have studied at the primary or middle school level, and only 7 percent have attended at least some secondary schooling. The traditional social structure of Pakistan supports a natural fertility pattern in which the majority of women do not use any means of fertility regulation. In such populations, the proximate determinants of fertility (other than contraception) are crucial in determining fertility levels. These include age at marriage, breastfeeding, and the duration of postpartum amenorrhoea and abstinence. The mean age at marriage has risen sharply over the past few decades, from under 17 years in the 1950s to 21.7 years in 1991. Despite this rise, marriage remains virtually universal: among women over the age of 35, only 2 percent have never married. Marriage patterns in Pakistan are characterised by an unusually high degree of consangninity. Half of all women are married to their first cousin and an additional 11 percent are married to their second cousin. Breasffeeding is important because of the natural immune protection it provides to babies, and the protection against pregnancy it gives to mothers. Women in Pakistan breastfeed their children for an average of20months. Themeandurationofpostpartumamenorrhoeais slightly more than 9 months. After tbebirth of a child, women abstain from sexual relations for an average of 5 months. As a result, the mean duration of postpartum insusceptibility (the period immediately following a birth during which the mother is protected from the risk of pregnancy) is 11 months, and the median is 8 months. Because of differentials in the duration of breastfeeding and abstinence, the median duration of insusceptibility varies widely: from 4 months for women with at least some secondary education to 9 months for women with no schooling; and from 5 months for women residing in major cities to 9 months for women in rural areas. In the PDHS, women were asked about their desire for additional sons and daughters. Overall, 40 percent of currently married women do not want to have any more children. This figure increases rapidly depending on the number of children a woman has: from 17 percent for women with two living children, to 52 percent for women with four children, to 71 percent for women with six children. The desire to stop childbearing varies widely across cultural groupings. For example, among women with four living children, the percentage who want no more varies from 47 percent for women with no education to 84 percent for those with at least some secondary education. Gender preference continues to be widespread in Pakistan. Among currently married non-pregnant women who want another child, 49 percent would prefer to have a boy and only 5 percent would prefer a girl, while 46 percent say it would make no difference. The need for family planning services, as measured in the PDHS, takes into account women's statements concerning recent and future intended childbearing and their use of contraceptives. It is estimated that 25 percent of currently married women have a need for family planning to stop childbearing and an additional 12 percent are in need of family planning for spacing children. Thus, the total need for family planning equals 37 percent, while only 12 percent of women are currently using contraception. The result is an unmet need for family planning services consisting of 25 percent of currently married women. This gap presents both an opportunity and a challenge to the Population Welfare Programme. Nearly one-tenth of children in Pakistan die before reaching their first birthday. The infant mortality rate during the six years preceding the survey is estimaled to be 91 per thousand live births; the under-five mortality rate is 117 per thousand. The under-five mortality rates vary from 92 per thousand for major cities to 132 for rural areas; and from 50 per thousand for women with at least some secondary education to 128 for those with no education. The level of infant mortality is influenced by biological factors such as mother's age at birth, birth order and, most importantly, the length of the preceding birth interval. Children born less than two years after their next oldest sibling are subject to an infant mortality rate of 133 per thousand, compared to 65 for those spaced two to three years apart, and 30 for those born at least four years after their older brother or sister. One of the priorities of the Government of Pakistan is to provide medical care during pregnancy and at the time of delivery, both of which are essential for infant and child survival and safe motherhood. Looking at children born in the five years preceding the survey, antenatal care was received during pregnancy for only 30 percent of these births. In rural areas, only 17 percent of births benefited from antenatal care, compared to 71 percent in major cities. Educational differentials in antenatal care are also striking: 22 percent of births of mothers with no education received antenatal care, compared to 85 percent of births of mothers with at least some secondary education. Tetanus, a major cause of neonatal death in Pakistan, can be prevented by immunisation of the mother during pregnancy. For 30 percent of all births in the five years prior to the survey, the mother received a tetanus toxoid vaccination. The differentials are about the same as those for antenatal care generally. Eighty-five percent of the births occurring during the five years preceding the survey were delivered

The purpose of this study was to investigate the impact of SEZ on indigenous peoples’ socioeconomic status and local development in the study area. A quantitative approach to analyzing the socioeconomics of treatment and control groups. A structured questionnaire was designed and a field survey was undertaken to collect primary data from respondents. This study used Principal Component Analysis (PCA) to create a socioeconomic index for two groups: those who sold their agricultural land and those who did not sell, and a two-sample independent t-test was used to determine the influence of SEZ on socioeconomic and local development. The results showed that the compensation amount for the acquired land not only improved the socio-economic living conditions of the indigenous population in short run, but also transformed their type of employment from agriculture to labor work, increased health expenditure, increased household wealth and minor changes in education expenditure and construction effected new houses, most of which is used for child marriage, vehicle purchased and dowary expenses in the special economic zone. This unproductive spending increases in the short term, which in the long run will convert skeikonicity into deprivation. Previous studies focused only on the geopolitics behind the geo-economy and the challenges and success factors for SEZs in Pakistan. This study is unique as it is the first attempt that uses statistical and economic tools to identify the positive and negative impacts of SEZ on the local development in the area. It makes an academic contribution to the literature to improve the knowledge of the effects of these special economic zones on the local development in any area.

Techsalerator’s Location Sentiment Data for Pakistan

Techsalerator’s Location Sentiment Data for Pakistan provides an extensive collection of real-time and historical sentiment insights, crucial for businesses, researchers, and analysts. This dataset helps in understanding public opinion, market trends, and regional sentiment variations across Pakistan.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Top 5 Key Data Fields

• Geographic Location – Captures sentiment data at the city, district, and regional levels, enabling localized analysis.
• Sentiment Score – Provides a numerical sentiment value (positive, neutral, negative) based on language processing algorithms.
• Topic Categorization – Classifies sentiment by industries such as politics, retail, healthcare, and technology.
• Source of Data – Aggregates sentiment from social media, news articles, customer reviews, and forums.
• Time-Stamped Sentiment Trends – Tracks how sentiment evolves over time for better trend analysis and forecasting.

Top 5 Location Sentiment Trends in Pakistan

• Political Sentiment Shifts – Regional differences in political sentiment vary widely, influencing election campaigns and policymaking.
• Consumer Market Insights – Retail sentiment analysis helps businesses understand consumer preferences and brand reputation.
• Tourism and Hospitality Sentiment – Feedback from travelers and locals provides insights into the evolving perception of tourist destinations.
• Economic and Business Confidence – Corporate sentiment analysis aids in investment decisions and market predictions.
• Social Issues and Public Opinion – Monitoring public discourse on key social issues helps organizations respond effectively to societal concerns.

Top 5 Applications of Location Sentiment Data in Pakistan

• Market Research & Consumer Behavior – Businesses can track consumer sentiment to adjust strategies and improve customer satisfaction.
• Political Campaigning & Governance – Governments and political parties can assess public perception to shape policies and campaigns.
• Crisis Management & Public Relations – Sentiment tracking helps brands and organizations respond effectively to crises and reputation risks.
• Investment & Economic Forecasting – Investors and analysts use sentiment trends to predict economic shifts and business performance.
• Tourism & City Planning – Authorities can use sentiment insights to enhance city infrastructure and promote tourism initiatives.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Pakistan, contact info@techsalerator.com with your specific requirements. Techsalerator provides customized datasets based on requested fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Geographic Location
• Sentiment Score
• Topic Categorization
• Source of Data
• Time-Stamped Sentiment Trends
• Emotion Analysis (Joy, Anger, Sadness, etc.)
• Industry-Specific Sentiment Insights
• Social Media and News Sentiment
• Customer Feedback Analysis
• Contact Information

For in-depth insights into sentiment trends across Pakistan, Techsalerator’s dataset is an invaluable resource for market researchers, businesses, policymakers, and analysts.

Pakistan PK: Out-of-Pocket Health Expenditure: % of Current Health Expenditure data was reported at 66.485 % in 2015. This records a decrease from the previous number of 66.525 % for 2014. Pakistan PK: Out-of-Pocket Health Expenditure: % of Current Health Expenditure data is updated yearly, averaging 66.505 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 78.016 % in 2006 and a record low of 57.527 % in 2002. Pakistan PK: Out-of-Pocket Health Expenditure: % of Current Health Expenditure data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Health Statistics. Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted Average;

Pakistan PK: Domestic General Government Health Expenditure: % of GDP data was reported at 0.738 % in 2015. This records an increase from the previous number of 0.698 % for 2014. Pakistan PK: Domestic General Government Health Expenditure: % of GDP data is updated yearly, averaging 0.688 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 1.155 % in 2002 and a record low of 0.527 % in 2006. Pakistan PK: Domestic General Government Health Expenditure: % of GDP data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Health Statistics. Public expenditure on health from domestic sources as a share of the economy as measured by GDP.; ; World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database).; Weighted average;

The 2012-13 Pakistan Demographic and Health Survey was undertaken to provide current and reliable data on fertility and family planning, childhood mortality, maternal and child health, women’s and children’s nutritional status, women’s empowerment, domestic violence, and knowledge of HIV/AIDS. The survey was designed with the broad objective of providing policymakers with information to monitor and evaluate programmatic interventions based on empirical evidence. The specific objectives of the survey are to: • collect high-quality data on topics such as fertility levels and preferences, contraceptive use, maternal and child health, infant (and especially neonatal) mortality levels, awareness regarding HIV/AIDS, and other indicators related to the Millennium Development Goals and the country’s Poverty Reduction Strategy Paper • investigate factors that affect maternal and neonatal morbidity and mortality (i.e., antenatal, delivery, and postnatal care) • provide information to address the evaluation needs of health and family planning programs for evidence-based planning • provide guidelines to program managers and policymakers that will allow them to effectively plan and implement future interventions