1. Data source: LSMS

LSMS surveys that had a health module. The most recent survey in a country is used, if there are multiple:
* Ethiopia 2018,
* Cambodia 2019,
* Malawi 2019,
* Nigeria 2018, and
* Tanzania 2019.
See Annex 1 for details.

2. What type of clients would we see for PREM survey?

  1. In terms of age and sex, overall, we will see many children and women in reproductive age (i.e., female and male < 15 + female 15-49).
  2. Reasons for visits data confirm that 40% of visits were related with MCH-type services (based on Ethiopia and Nigeria data).
  3. In hospitals, however, we will see more older clients (based on Ethiopia and Nigeria data).
  4. The distribution will vary mostly depending on the fertility level (e.g., compare age distributions). Although illness is more common among elderlies and care-seeking is higher among elderlies, the overall population age structure is the main factor to determine overall client profile.
  5. Unless higher level facilities are targeted, there may not be many visits for which continuity and/or coordination are relevant - based on Ethiopia data (which have more granular information than Nigeria).

2.1. Background characteristics, among those who visited health facilities

  • Includes both those who had any visits AND those who visited for recent illness (see annex).
NOTE: 
* 4-week reference period for Ethiopia, Cambodia, and Nigeria   
* 2-week reference period for Malawi. Also, Malawi did NOT collect "any visit" data. i.e., only care-seeking for illness included

2.2. Background characteristics by facility type, among those who visited health facilities for recent illness in the past 4 weeks

2.3. Reasons for any visits, among those who visited health facilities

  • Available only in Ethiopia and Nigeria
Ethiopia
Reasons for any visits in the last 4 weeks (all ages)
xreason_1 n percentage coordination.continuity
  1. CHECK UP OR OTHER PREVENTIVE CARE (NOT LINKED TO PREGNANCY)
150 5.2 V
  1. PRENATAL CHECKUP
106 3.7 V
  1. GIVING BIRTH
41 1.4
  1. FOLLOW UP APPOINTMENT FOR EARLIER OR CHRONIC ILLNESS
495 17.3 V
  1. FOLLOWUP APPOINTMENT FOR EARLIER ACCIDENT
90 3.1 V
  1. NEW OR ACUTE ILLNESS
1846 64.5
  1. NEW INJURY
125 4.4
  1. OTHER (SPECIFY)
11 0.4
Nigeria
Reasons for any visits in the last 4 weeks (all ages)
xreason_1 n percentage coordination.continuity
  1. GENERAL CHECKUP (NOT FOR PREGNANCY)
238 5.0 V
  1. PRE/POSTNATAL CHECKUP
84 1.8 V
  1. GIVING BIRTH
24 0.5
  1. ILLNESS
4207 88.9
  1. INJURY
178 3.8

Annex 1: LSMS data files

GHA_2009_GSPS_v01_M_CSV https://microdata.worldbank.org/index.php/catalog/2534/data-dictionary

KHM_2019_LSMS-PLUS_v02_M https://microdata.worldbank.org/index.php/catalog/4045/data-dictionary

MWI_2019_IHS-V_v05_M_CSV: https://microdata.worldbank.org/index.php/catalog/3818/data-dictionary

NGA_2018_GHSP-W4_v03_M: https://microdata.worldbank.org/index.php/catalog/3557/data-dictionary

TZA_2019_NPS-SDD_v06_M: https://microdata.worldbank.org/index.php/catalog/3885

Note considerable variations in contents, question order and reference period. Most are part of panel surveys.

Ethiopia
* sect_cover_hh_w4: Household identification; location; household size, and field staff identification.
* sect1_hh_w4: Roster - List of individuals living in the household and basic demographics; for members younger than 18, parental education and occupation.
* sect3_hh_w4: Health - Health problems, types of injury/illness, medical assistance/consultation, health insurance, disabilities, vital registration (birth certificate), breast feeding, and anthropometrics (children and under).

Ghana
* key_hhld_info
* S1D
* S6F: Health in the last 2 Weeks Recent illness or injury, visit to a health provider
* S6G: Health in the last 12 month Medical expenses, tablets, etc.

Cambodia
* hh_sec_1: File contains data from Section 1: Household identification
* hh_sec_2: File contains data from Section 2: Household roster
* hh_sec_12: File contains data from Section 12: Healthcare seeking and expenditure; Subsection 12 B: Illness and healthcare expenditure during the last 30 days

Malawi
* hh_mod_a_filt.dta: Data collected through Household Questionnaire, Module A: Household Identification (household level data)
* HH_MOD_B.dta: Data collected through Household Questionnaire, Module B: Household Roster ( individual level data)
* HH_MOD_D.dta: Data collected through Household Questionnaire, Module D: Health (individual level data)

Nigeria
* sectaa_harvestw4: Data collected through Post Harvest Agriculture Questionnaire, Household Identification
* sect1_plantingw4: Data collected through Post Planting Household Questionnaire, Section 1 (Roster)
* sect4a_harvestw4: Data collected through Post-Harvest Household Questionnaire, Section 4 (Health)

Tanzania
* HH_SEC_A: Household location variables, unique within panel round household identification variables, date and time of interview, analytic sampling weights, cluster identification, sampling strata identification, and status of survey.
* HH_SEC_B: Roster of household members, individual characteristics
* HH_SEC_D: General health status and utilization of health services; source and financing of health treatments / hospitalization, disaggregated health expenditures, disability, bednet use, pregnancy, prenatal care and births, child health and ailments / diarrhoea.

Annex 2: Further descriptive data

Overall care-seeking level

Percentage of population having illness vs. Percentage of population having illness AND used health services

Among population with illness, percentage having used health services

Population distribution

Background characteristics, among those who visited health facilities for recent illness

Where comparison is possible (Ethiopia and Nigeria), the distributions are similar among all visits (above) and among visits for recent illness.

  • 4-week reference period for Ethiopia, Cambodia, and Nigeria
  • 2-week reference period for Malawi

Reasons for visits, among those who visited health facilities for recent illness

END OF MARKDOWN